Despite the tremendous advances that have occured in mapping genes for mendelian diseases, the discovery of genes that influence susceptibility to more common human diseases, particularly the neuropsychiatric disorders, has proceeded slowly The disease susceptibility of these disorders is very complex, and is likely influenced by mulitple genes having mall effects as well as many interacting environmental factors. Furthermore, population stratification, a major confounding factor in detecting disease- marker associations, presents a particular challenge to dissecting complex traits. Novel and powerful statistical methods are needed to take full advangtage of the enormous amount of information invaluable for human linkage studies that has accumulated over the past few years, including both highly informative markers and dense maps of those markers. The major objective of this proposal is to extend and develop powerful statistical methods to detect linkage to genes underlying complex traits that are robust to population stratification. Specifically, we propose to develop: (1) transmission disequilibrium tests that can jointl analyze multiple markers within a short physical distance on a chromosome, yielding far greater power than single-marker methods; (2) a unified framework for the transmission/disequilibrium test that is applicable to more general family structures than those that can be analyzed by existing methods; and (3) statistical methods for modeling and distinguishing different gene-gene interactions and gene-environment interactions. Both theoretical studies and extensive computer simulations will be carried out to evaluate and compare these methods with other approaches. The developed methodology will be applied to available data sets to detect linkage and to understand the complex etiologies of the traits of interest. Well-documented and efficient computer programs that implement the new and powerful methods will be extensively developed and tested under different computer operating systems. These programs will be made widely available to the scientific community through the World Wide Web. The new statistical methods and the powerful computer programs will provide biomedical researchers with important tools to extreact the maiximal information from the enormous amount of genomic information generated to map genes for complex traits and to understand the complex etiologies underlying these traits.